A large number of images exist at various locations accessible on the Internet. Some of these images have an associated geocode that indicates a particular location at which the image was captured or a particular location that the image depicts. For example, such locations may be indicated by a tag generated based on GPS data or may be locations generated or assigned by a user. Therefore, such images having geocodes can quickly and easily be organized or searched based on location. Thus, such images are more easily accessible for a person attempting to explore or visually learn about a particular location.
However, a significant percentage of the existing images do not have an associated geocode that identifies a particular location associated with the image. The absence of such location information makes it difficult to accurately organize such images based on location.
Certain existing systems for determining geocodes have inherent limitations. For example, matching images to a location based on a computerized analysis of the image content can require significant computing resources and, therefore, may be impractical for application to the entirety of the world's images. In addition, many images may not provide explicit location-specific visual content that is identifiable using existing algorithms.
As another example, determining a location for an image based solely on the textual content included in a web document that includes such image can lead to a significant number of images being miscategorized or not categorized at all. For example, analysis of a travel blog that includes imagery and text associated with a trip across multiple European countries may lead to certain of the images being geocoded to an incorrect location based on the surrounding text discussing a different portion of the trip
Therefore, systems and methods that can accurately, quickly, and easily determine a geocode for an image are desirable.